By analyzing the internal features of counting sorting algorithm. Two improvements of counting sorting algorithms are proposed, which have a wide range of applications and better efficiency than the original counting ...By analyzing the internal features of counting sorting algorithm. Two improvements of counting sorting algorithms are proposed, which have a wide range of applications and better efficiency than the original counting sort while maintaining the original stability. Compared with the original counting sort, it has a wider scope of application and better time and space efficiency. In addition, the accuracy of the above conclusions can be proved by a large amount of experimental data.展开更多
Remaining useful life(RUL)is a significant challenge in prognostics and health management.Existing methods suffer from a severe performance drop,as testing data from engine sensors exhibits high nonlinearity and compl...Remaining useful life(RUL)is a significant challenge in prognostics and health management.Existing methods suffer from a severe performance drop,as testing data from engine sensors exhibits high nonlinearity and complicated fault modes.In this paper,the authors introduce a reinforcement neural architecture search technique based on upper confidence bound(UCB)to optimize an efficient model.UCB explores the combinatorial parameter space of a multi-head convolutional layers concatenate with recurrent layers to search for a suitable architecture.To address the highly nonlinear dataset in complicated working conditions,rainflow counting algorithm is applied to extract features.Experiments are conducted on C-MAPSS dataset.Compared with state-of-the-art,the proposed approach yields better results in both RMSE and scoring function for all the sub-datasets.In multiple working conditions,the authors achieve lower RMSE with significant superiority.The experimental results confirm that the proposed method is an efficient approach for obtaining highly precise RUL predictions.展开更多
Due to the diversity of graph computing applications, the power-law distribution of graph data, and the high compute-to-memory ratio, traditional architectures face significant challenges regarding poor flexibility, i...Due to the diversity of graph computing applications, the power-law distribution of graph data, and the high compute-to-memory ratio, traditional architectures face significant challenges regarding poor flexibility, imbalanced workload distribution, and inefficient memory access when executing graph computing tasks. Graph computing accelerator, GraphApp, based on a reconfigurable processing element(PE) array was proposed to address the challenges above. GraphApp utilizes 16 reconfigurable PEs for parallel computation and employs tiled data. By reasonably dividing the data into tiles, load balancing is achieved and the overall efficiency of parallel computation is enhanced. Additionally, it preprocesses graph data using the compressed sparse columns independently(CSCI) data compression format to alleviate the issue of low memory access efficiency caused by the high memory access-to-computation ratio. Lastly, GraphApp is evaluated using triangle counting(TC) and depth-first search(DFS) algorithms. Performance analysis is conducted by measuring the execution time of these algorithms in GraphApp against existing typical graph frameworks, Ligra, and GraphBIG, using six datasets from the Stanford Network Analysis Project(SNAP) database. The results show that GraphApp achieves a maximum performance improvement of 30.86% compared to Ligra and 20.43% compared to GraphBIG when processing the same datasets.展开更多
文摘By analyzing the internal features of counting sorting algorithm. Two improvements of counting sorting algorithms are proposed, which have a wide range of applications and better efficiency than the original counting sort while maintaining the original stability. Compared with the original counting sort, it has a wider scope of application and better time and space efficiency. In addition, the accuracy of the above conclusions can be proved by a large amount of experimental data.
基金supported by the National Natural Science Foundation of China under Grant Nos.62073197and 61933006。
文摘Remaining useful life(RUL)is a significant challenge in prognostics and health management.Existing methods suffer from a severe performance drop,as testing data from engine sensors exhibits high nonlinearity and complicated fault modes.In this paper,the authors introduce a reinforcement neural architecture search technique based on upper confidence bound(UCB)to optimize an efficient model.UCB explores the combinatorial parameter space of a multi-head convolutional layers concatenate with recurrent layers to search for a suitable architecture.To address the highly nonlinear dataset in complicated working conditions,rainflow counting algorithm is applied to extract features.Experiments are conducted on C-MAPSS dataset.Compared with state-of-the-art,the proposed approach yields better results in both RMSE and scoring function for all the sub-datasets.In multiple working conditions,the authors achieve lower RMSE with significant superiority.The experimental results confirm that the proposed method is an efficient approach for obtaining highly precise RUL predictions.
基金supported by the National Science and Technology Major Project (2022ZD0119001)the National Natural Science Foundation of China (61834005)+1 种基金the Shaanxi Key Research and Development Project (2022GY-027)the Key Scientific Research Project of Shaanxi Department of Education (22JY060)。
文摘Due to the diversity of graph computing applications, the power-law distribution of graph data, and the high compute-to-memory ratio, traditional architectures face significant challenges regarding poor flexibility, imbalanced workload distribution, and inefficient memory access when executing graph computing tasks. Graph computing accelerator, GraphApp, based on a reconfigurable processing element(PE) array was proposed to address the challenges above. GraphApp utilizes 16 reconfigurable PEs for parallel computation and employs tiled data. By reasonably dividing the data into tiles, load balancing is achieved and the overall efficiency of parallel computation is enhanced. Additionally, it preprocesses graph data using the compressed sparse columns independently(CSCI) data compression format to alleviate the issue of low memory access efficiency caused by the high memory access-to-computation ratio. Lastly, GraphApp is evaluated using triangle counting(TC) and depth-first search(DFS) algorithms. Performance analysis is conducted by measuring the execution time of these algorithms in GraphApp against existing typical graph frameworks, Ligra, and GraphBIG, using six datasets from the Stanford Network Analysis Project(SNAP) database. The results show that GraphApp achieves a maximum performance improvement of 30.86% compared to Ligra and 20.43% compared to GraphBIG when processing the same datasets.
